diff --git a/code_bert_score/score.py b/code_bert_score/score.py
index e74a6a8..b65a178 100644
--- a/code_bert_score/score.py
+++ b/code_bert_score/score.py
@@ -164,10 +164,10 @@ def score(
                 baselines = torch.from_numpy(pd.read_csv(baseline_path).iloc[num_layers].to_numpy())[1:].float()
             else:
                 baselines = torch.from_numpy(pd.read_csv(baseline_path).to_numpy())[:, 1:].unsqueeze(1).float()
-
-            all_preds = (all_preds - baselines) / (1 - baselines)
         else:
-            print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+            # print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+            baselines = 0.5
+        all_preds = (all_preds - baselines) / (1 - baselines)
 
     out = all_preds[..., 0], all_preds[..., 1], all_preds[..., 2], all_preds[..., 3]  # P, R, F, F3
 
@@ -252,9 +252,11 @@ def plot_example(
             baseline_path = os.path.join(os.path.dirname(__file__), f"rescale_baseline/{lang}/{model_type}.tsv")
         if os.path.isfile(baseline_path):
             baselines = torch.from_numpy(pd.read_csv(baseline_path).iloc[num_layers].to_numpy())[1:].float()
-            sim = (sim - baselines[2].item()) / (1 - baselines[2].item())
+            baseline = baselines[2].item()
         else:
-            print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+            # print(f"Warning: Baseline not Found for {model_type} on {lang} at {baseline_path}", file=sys.stderr)
+            baseline = 0.5
+        sim = (sim - baseline) / (1 - baseline)
 
     import matplotlib.pyplot as plt
     from mpl_toolkits.axes_grid1 import make_axes_locatable
diff --git a/example.py b/example.py
index 21a90bd..381c229 100644
--- a/example.py
+++ b/example.py
@@ -56,16 +56,19 @@ def print_results(predictions, refs, pred_results):
     with open('idf_dicts/java_idf.pkl', 'rb') as f:
         java_idf = pickle.load(f)
 
-    # pred_results = code_bert_score.score([''],['a'], sources=["a"], lang="python")
-    # pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf)
-    # print_results(predictions, refs, pred_results)
+    pred_results = code_bert_score.score([''],['a'], sources=["a"], lang="python")
+    pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf)
+    print_results(predictions, refs, pred_results)
 
-    # print('When providing the context: "find the index of target in this.elements"')
-    # pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf, sources=['find the index of target in this.elements'] * 2)
-    # print_results(predictions, refs, pred_results)
+    print('When providing the context: "find the index of target in this.elements"')
+    pred_results = code_bert_score.score(cands=predictions, refs=refs, no_punc=True, lang='java', idf=java_idf, sources=['find the index of target in this.elements'] * 2)
+    print_results(predictions, refs, pred_results)
 
 
     with open('idf_dicts/python_idf.pkl', 'rb') as f:
         python_idf = pickle.load(f)
     pred_results = code_bert_score.score(cands=['math.sqrt(x)'], refs=[['x ** 0.5']], no_punc=True, lang='python', idf=python_idf)
+    print(pred_results)
+
+    pred_results = code_bert_score.score(cands=['math.sqrt(x)'], refs=[['x ** 0.5']], rescale_with_baseline=True, lang='en')
     print(pred_results)
\ No newline at end of file